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Using the n-gram based document vector was found to slightly improve performance during the stratified partition validation at the expense of an increase in training time and resource usage (3 minutes to completion for BoW and 9 minutes to completion for n-grams on the same machine with the same data, increase of approximately 300%), while performance only increased by 2.5% on average.
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How to cite this article: Legg, D. A. et al. Arthropod fossil data increase congruence of morphological and molecular phylogenies.
However, the requirement of data increase the complex of hardware platform.
But as the dimensionality of data increases, some of the dimensions become irrelevant to some of the clusters.
These data increase our understanding of the genetic basis of rainbow trout BCWD resistance.
When the perceived value of data increases, the sense of ownership of data increases, as well, and programs improve (10, 13– 13).
Also, the triangulation of participant and HCP data increased validity of our results.
Obviously, as the amount of training data increase (displayed as the time period of data increases), more VMP can be discovered and PU goes up.
Consistent with the western data, increased expression of TIP60 was also observed in the garcinol-treated cells, but not controls.
The amount and complexity of scientific data increase exponentially.
The requirement of data increases the burden of the radar system.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com